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120 results for “CATASTROPHIZING”
Fish and crayfish density and count data for Peeks Creek, Macon County, NC, USA 2005-2014, 2019, and 2022 following a catastrophic debris flow, as well as six reference streams
We followed the process of recovery of the fish and crayfish assemblage in Peeks Creek, a high-gradient second order stream in the Little Tennessee River watershed of North Carolina, after a debris flow devastated the channel and its riparian zone. After 15 years, the fish assemblage had recovered, and the channel and riparian zone had stabilized. Of the three major components of the fish assemblage, Rainbow Trout (Oncorhynchus mykiss (Walbaum)), a strong swimmer, reappeared in year 1. Longnose Dace (Rhinichthys cataractae (Valenciennes in Cuvier and Valenciennes)) reappeared in year 3. Mottled Sculpin (Cottus bairdii Girard), a weak swimmer, did not become established until year 6 and only resumed expected abundance in year 9. Appalachian Brook Crayfish (Cambarus bartonii cavatus Hay) numbers recovered quickly, though only one individual was found the year following the debris flow. Unassisted natural recovery occurred after a costly engineered restoration project had been rejected and arguably represents the preferable solution. However, recovery of the fish assemblage may not have been achieved if the stream flowed directly into an impoundment or low gradient river that lacked the source of species for recolonization, or if the stream had been located above a barrier to upstream movement.
Pain Catastrophizing Predicts Alcohol Hangover Severity and Symptoms - Raw Data
<p>Raw data from investigation published as 'Pain Catastrophizing Predicts Alcohol Hangover Severity and Symptoms.'</p>
Trait-based sensitivity of large mammals to a catastrophic tropical cyclone: DNA metabarcoding data
<p>Extreme weather events perturb ecosystems and increasingly threaten biodiversity<sup>1</sup>. Ecologists emphasize the need to forecast and mitigate the impacts of these incidents, which requires knowledge of how risk is distributed among species and environments, but the scale and unpredictability of extreme events complicates assessment<sup>1</sup><sup>–4</sup>. These challenges are compounded for large animals ('megafauna'), which play crucial ecological roles but are hard to study<sup>5</sup>. Traits such as body size, dispersal ability, and habitat affiliation are among the hypothesized determinants of animals' vulnerability to natural hazards<sup>1,6,7</sup>. However, it has rarely been possible to test these propositions or, more generally, to link short- and longer-term effects of weather-related disturbance<sup>8,9</sup>. Here, we show how large herbivores and carnivores in Mozambique responded to Intense Tropical Cyclone Idai, the deadliest storm on record in Africa, across scales ranging from individual decisions in the hours after landfall to community-level responses nearly 20 months later. Animals occupying low-elevation habitats exhibited strong spatial responses to rising floodwaters. Body size predicted species' subsequent numerical responses: small-bodied species exhibited the greatest population declines. We trace this sensitivity to limited mobility, which increased likelihood of death during the flood and constrained animals' capacity to withstand food shortages afterward. Our results identify potentially general trait-based mechanisms underlying animal responses to severe weather and may help to inform strategies for wildlife conservation in a volatile climate.</p> <ol> <li><span><em><span>Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change</span></em><span> [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)]. Cambridge University Press. Cambridge University Press, Cambridge, UK and New York, NY, USA, (2022).</span></span></li> <li><span><span>Smith, M. An ecological perspective on extreme climatic events: A synthetic definition and framework to guide future research. <em>J. Ecol.</em> <strong>99</strong>, 656-663 (2011).</span></span></li> <li><span><span>Ummenhofer, C. C., & Meehl, G. A. Extreme weather and climate events with ecological relevance: a review, <em>Phil. Trans. R. Soc. B. </em><strong>372</strong>, 20160135 (2017).</span></span></li> <li><span><span>Jentsch, A., Kreyling, J., & Beierkuhnlein, C. A new generation of climate-change experiments: events, not trends. <em>Front. Ecol. Environ. </em><strong>5</strong>, 365-374 (2007).</span></span></li> <li><span><span>Pringle, R. M., et. al. Impacts of large herbivores on terrestrial ecosystems. <em>Current Biology</em> <strong>33</strong>, R584-R610 (2023).</span></span></li> <li><span><span>Spiller, D. A., Losos, J. B., & Schoener, T. W. Impact of a catastrophic hurricane on island populations. <em>Science </em><strong>281</strong>, 695-697 (1998). </span></span></li> <li><span><span>Schoener, T. W., & Spiller, D. A. Nonsynchronous recovery of community characteristics in island spiders after a catastrophic hurricane. <em>PNAS </em><strong>103</strong>, 2220-2225 (2006).</span></span></li> <li><span><span>Pruitt, N., Little, A. G., Majumdar, S. J., Schoener, T. W., & Fisher, D. N. Call-to-Action: A global consortium for tropical cyclone ecology. <em>TREE </em><strong>34</strong>, 588-590 (2019).</span></span></li> <li><span><span>Lin, T. C., Hogan, J. A., & Chang, C. T. Tropical cyclone ecology: a scale-link perspective. <em>TREE </em><strong>35</strong>, 594-604 (2020).</span></span></li> </ol>
Shapefiles with the outline of maximum water spread resulting from the catastrophic release of the Kakhovka Reservoir after the destruction of the Kakhovka Hydroelectric Power Plant by Russian occupying forces
<p>The map is based on remote sensing data from Sentinel-2A (Processing Level L2A), dated June 8, June 13, and June 18, 2023, and Landsat-9 (Collection 2 Level-1), dated June 9, 2023. </p> <p>The following Sentinel-2 remote sensing data granules were used:<br>S2A_MSIL2A_20230608T084601_N0509_R107_T36TUS_20230608T132103.SAFE S2A_MSIL2A_20230608T084601_N0509_R107_T36TVS_20230608T132103.SAFE<br>S2A_MSIL2A_20230608T084601_N0509_R107_T36TWS_20230608T132103.SAFE<br>S2A_MSIL2A_20230608T084601_N0509_R107_T36TVT_20230608T132103.SAFE<br>S2B_MSIL2A_20230613T084609_N0509_R107_T36TUS_20230613T102806.SAFE<br>S2B_MSIL2A_20230613T084609_N0509_R107_T36TVS_20230613T102806.SAFE<br>S2B_MSIL2A_20230613T084609_N0509_R107_T36TWS_20230613T102806.SAFE<br>S2A_MSIL2A_20230618T084601_N0509_R107_T36TUS_20230618T151602.SAFE<br>S2A_MSIL2A_20230618T084601_N0509_R107_T36TVS_20230618T151602.SAFE<br>S2A_MSIL2A_20230618T084601_N0509_R107_T36TWS_20230618T151602.SAFE</p> <p>The following remote sensing data scenes from Landsat-9 were used:<br>LC09_L1TP_179028_20230609_20230610_02_T1<br>LC09_L1TP_179027_20230609_20230610_02_T1</p> <p>The contour of the maximum water spread was constructed using a method of manual visual interpretation of remote sensing data, relying on knowledge of the local terrain. We consciously chose not to use automated methods with water indices such as the Normalized Difference Water Index (NDWI) or the Modified Normalized Difference Water Index (MNDWI), as these do not effectively distinguish water surfaces in areas covered with forest or dense reed thickets. Similarly, we did not use the SRTM digital elevation model due to significant artifacts in the study area, where the model shows the height of the forest canopy instead of the ground surface in forested areas.</p> <p>For visual interpretation of Sentinel-2A remote sensing data, we used combinations of spectral bands NIR-Red-Green (8-4-3) and SWIR2-NIR-Green (12-8-3). For the visual interpretation of Landsat-9 remote sensing data, we used combinations of bands SWIR1-NIR-Red (6-5-4) and NIR-Red-Green (5-4-3). To better align the resolution of Sentinel-2A remote sensing data (10 m/pixel) with that of Landsat-9 (30 m/pixel), the latter's data was enhanced using the panchromatic channel (Band 8) through IHS-based pansharpening to 15 m/pixel. The pansharpening was performed using a custom bash script, utilizing command-line tools and utilities such as ImageMagick (<a href="https://imagemagick.org" rel="nofollow">https://imagemagick.org</a>), listgeo, and geotifcp (<a href="https://github.com/OSGeo/libgeotiff">https://github.com/OSGeo/libgeotiff</a>). To expedite the pansharpening process, both Landsat scenes were cropped to the study region and merged by bands using the gdal_translate and gdal_merge.py utilities from the GDAL library (<a href="https://gdal.org/" rel="nofollow">https://gdal.org/</a>). For convenience, the Sentinel-2A data tiles T36TUS, T36TVS, and T36TWS were also cropped and merged by bands using custom scripts available at <a href="https://doi.org/10.5281/zenodo.13205058" rel="nofollow">https://doi.org/10.5281/zenodo.13205058</a>.</p> <p>During visual interpretation, the above-mentioned remote sensing data were compared with satellite images acquired before the destruction of the Kakhovka Hydroelectric Power Plant. In particular, Landsat-9 remote sensing data were compared with Landsat-8 data from June 1, 2023, and Sentinel-2A data were compared with Sentinel-2B data from June 3, 2023.</p> <p>Repository files:<br>floodMax_UTM36N.zip — contains the shapefile in UTM36N projection (EPSG:32636);<br>floodMax_WGS84.zip — contains the shapefile in geographic coordinates in WGS84 (EPSG:4326);<br>floodMax_WGS84.geojson.zip — contains a GeoJSON file in WGS84 coordinates (EPSG:4326).</p> <div> <h1>Web version of the map</h1> </div> <p>The web version of the maximum water spread map is available at:<br><a href="https://yumoskalenko.github.io/floodmap_Kakhovka2023/" rel="nofollow">https://yumoskalenko.github.io/floodmap_Kakhovka2023/</a></p> <p> </p> <p>Embed code for the map on a webpage:</p> <div> <pre><code><iframe style="border: 1px solid black" src="https://yumoskalenko.github.io/floodmap_Kakhovka2023/index.html" marginwidth="0" marginheight="0" scrolling="no" width="100%" height="360" frameborder="0"></iframe> </code></pre> <div> </div> </div> <p><em><strong>This scientific and technical product was created by the scientists of the Black Sea Biosphere Reserve of the National Academy of Sciences of Ukraine during the implementation of research on the topic "Monitoring the condition of natural complexes of the Black Sea Biosphere Reserve (‘Chronicle of Nature’)" (state registration number 0121U109174).</strong></em></p>
galacticwinds/Catastrophic_Cooling_Superwinds_III_Data: Catastrophic-Cooling-Superwinds-III-Data.1.1
<p><a href="https://galacticwinds.github.io/Catastrophic_Cooling_Superwinds_III_Data/"><strong>Catastrophic Cooling in Superwinds. III. Non-equilibrium Photoionization, Supplementary Material</strong></a></p> <p>The files of the interactive figure (<em>192 images</em>) of <strong>Figure 3</strong>, and the compressed (tar.gz) file of the <em>3 machine-readable table files</em> (a portion shown for guidance regarding its form and content in <strong>Table 1</strong>) are archived on Zenodo.</p> <ul> <li><a href="https://galacticwinds.github.io/Catastrophic_Cooling_Superwinds_III_Data/figure3/"><strong>Figure 3</strong></a>. Top Panels: The hydrogen temperature profiles (left panels) along with the adiabatic prediction (red dashed line) in the CPI and NPI cases, and the line emissivities (right panels) of the superwind models on a logarithmic scale, from top to bottom, in the CPI and NPI cases. Bottom Panels: The hydrogen density profiles (left panels) along with the adiabatic prediction (red dashed lines), and the ionic fractions (right panels) on a logarithmic scale for the CPI and NPI cases. The start and end of the hot bubble (region b), the end of the shell (region c), and the Strömgren radius are shown by dotted, dashed, dash-dotted, and solid lines (gray color), respectively.</li> <li><a href="https://galacticwinds.github.io/Catastrophic_Cooling_Superwinds_III_Data/table1/"><strong>Table 1</strong></a>. Integrated luminosities (unit in erg/s) from the different ionization models.</li> </ul> <p><strong>Citation</strong></p> <pre><code class="language-bash">@article{Danehkar2021, author = {{Danehkar}, A. and {Oey}, M.~S. and {Gray}, W.~J.}, title = {Catastrophic Cooling in Superwinds. III. Nonequilibrium Photoionization}, journal = {ApJ, accepted}, volume = {937}, number = {2}, pages = {68}, year = {2022}, doi = {10.3847/1538-4357/ac8cec} }</code></pre> <p><strong>Learn More</strong></p> <p>Website: <a href="https://superwinds.astro.lsa.umich.edu/">https://superwinds.astro.lsa.umich.edu/</a><br> GitPage: <a href="https://galacticwinds.github.io/">https://galacticwinds.github.io/</a></p>
Equation of state tables used in the paper: "Exploring the Catastrophic Regime: Thermodynamics and Disintegration in Head-On Planetary Collisions"
<p>The EoS (Equation of State) tables for iron and forsterite used in the paper "<span>Exploring the Catastrophic Regime: Thermodynamics and Disintegration</span><br><span>in Head-On Planetary Collisions</span>" are available here for use.</p> <p>These tables can be directly integrated into the SPH (Smoothed Particle Hydrodynamics) code <a href="https://swift.strw.leidenuniv.nl/">SWIFT</a> to conduct simulations of planetary giant impacts.</p> <p>The tables were generated using the GitHub repository maintained by Sarah T. Stewart, specifically for <a href="https://github.com/ststewart/aneos-iron-2020">iron</a> and <a href="https://github.com/ststewart/aneos-forsterite-2019">forsterite</a>.</p> <p>All tables were created with the tension regions of the materials removed.</p> <p>Table "ANEOS_iron_S20_100gcc_denseTgrid_NOTension.txt" was generated with denser grid at temperate range 1e5 to 1e6 to using for impacts with target mass above 10 Earth mass.</p> <p>Table "ANEOS_forsterite_S19_80gcc_NOTension.txt" was generated with dense grid in both rho and temperate dimention.</p> <p>"ANEOS_iron_S20_100gcc_denseTgrid_NOTension.txt" and "ANEOS_forsterite_S19_80gcc_NOTension.txt" were generated to deal with extreme cases that impact speeds and target masses are very high. </p> <p>Most of the simulations were run with "ANEOS_forsterite_S19.txt" and "ANEOS_iron_S20.txt".</p> <p> </p>
galacticwinds/Catastrophic_Cooling_Superwinds_II_Data: Catastrophic-Cooling-Superwinds-II-Data.1.2
<p><a href="https://galacticwinds.github.io/Catastrophic_Cooling_Superwinds_II_Data/"><strong>Catastrophic Cooling in Superwinds. II. Exploring the Parameter Space, Supplementary Material</strong></a></p> <p>The files of the interactive figure (<em>64 images</em>) of <strong>Figure 2</strong>, the interactive animation (<em>64 videos</em>) of <strong>Figure 3</strong>, the interactive figure (<em>192 images</em>) of <strong>Figure 6</strong>, and the compressed (tar.gz) file of the <em>6 machine-readable table files</em> (a portion shown for guidance regarding its form and content in <strong>Table 3</strong>) are archived on Zenodo.</p> <ul> <li><a href="https://galacticwinds.github.io/Catastrophic_Cooling_Superwinds_II_Data/figure2/"><strong>Figure 2</strong></a>. From top to bottom: the interactive figure of the temperature and density profiles of the superwinds on a logarithmic scale. The different regions separated by dotted lines: (a) freely expanding wind, (b) shocked-wind, (c) shell, and (d) ambient medium. The adiabatic temperature and density profile for each model are shown by dashed lines.</li> <li><a href="https://galacticwinds.github.io/Catastrophic_Cooling_Superwinds_II_Data/figure3/"><strong>Figure 3</strong></a>. From top to bottom: the interactive animation of the temperature and density profiles of the superwinds on a logarithmic scale. The different regions separated by dotted lines: (a) freely expanding wind, (b) shocked-wind, (c) shell, and (d) ambient medium. The adiabatic temperature and density profile for each model are shown by dashed lines.</li> <li><a href="https://galacticwinds.github.io/Catastrophic_Cooling_Superwinds_II_Data/figure6/"><strong>Figure 6</strong></a>. Top Panels: The hydrogen temperature profiles (left panels) along with the adiabatic prediction (red dashed line) in the CPI case, and the line emissivities (right panels) of the superwind models on a logarithmic scale, from top to bottom, in the PI and CPI cases. Bottom Panels: The hydrogen density profiles (left panels) along with the adiabatic prediction (red dashed lines), and the ionic fractions (right panels) on a logarithmic scale for the PI and CPI cases. The start and end of the hot bubble (region b), the end of the shell (region c), and the Strömgren radius are shown by dotted, dashed, dash-dotted, and solid lines (gray color), respectively.</li> <li><a href="https://galacticwinds.github.io/Catastrophic_Cooling_Superwinds_II_Data/table3/"><strong>Table 3</strong></a>. Integrated luminosities (unit in erg/s) from the different ionization models.</li> </ul> <p><strong>Citation</strong></p> <pre><code class="language-bash">@article{Danehkar2021, author = {{Danehkar}, Ashkbiz and {Oey}, M.~S. and {Gray}, William~J.}, title = {Catastrophic Cooling in Superwinds. II. Exploring the Parameter Space}, journal = {ApJ}, volume = {921}, number = {1}, pages = {91}, year = {2021}, doi = {10.3847/1538-4357/ac1a76} }</code></pre> <p><strong>Learn More</strong></p> <p>Website: <a href="https://superwinds.astro.lsa.umich.edu/">https://superwinds.astro.lsa.umich.edu/</a><br> GitPage: <a href="https://galacticwinds.github.io/">https://galacticwinds.github.io/</a></p>
Catastrophic Public Awakening in PRC upon Countless non-COVID-19 Deaths
<p>With the PRC regime mandate of quarantine trying to justify its individually targetable mass surveillance system, the causal deaths tolled for the measurements in the stead of the COVID-19 itself. Three children were locked alone at home on November 25 EST+8, 2022 for the quarantine measurements, and a fire killed them all the same night. People gathered on the streets and on social media people shared the details of multiple perspectives and experiences. Gunshots were fired reminding the population of the 1989 Tiananmen Square Massacre. Overnight Chinese cyber police have been deleting information on a mass scale, and the data consist of some collection saved by the researcher's acquaintance.</p>
Code and data from "Old reserves and ancient buds fuel regrowth of coast redwood after catastrophic fire"
<p>This repository includes:</p><p>[1] R code required to produce Fig 3 panels b and c, that is, the analysis of mixture composition of the oldest sprout 14c mean age observed in our study. </p><p>[2] Also included for convenience is the Hua et al 2022 annual atmospheric record of 14c for convenience. This is publicially available and citation is given directly in code.</p><p>[3] Data used in supplemental analyses of correlations among sprout age and tree-level covariates "Peltier_supp_dat.csv" containing columns in order: tree number, DBH (cm), height (m) vigor estimate, Feb 2021 dated sprout 14c age, July 2021 dated sprout 14c age, uncertainty in Feb2021, uncertainty in July2021, total sapwood NSC concentration (phenol method), total sapwood NSC concentration (enzyme method), phloem respired 14c age, shallow sapwood respired 14c age, deep sapwood respired 14c age, phloem C respired mass (mg), shallow C respired mass (mg), deep sapwood C respired mass (mg), sapwood ring count, and sapwood depth (mm).</p>
Trait-based sensitivity of large mammals to a catastrophic tropical cyclone: DNA metabarcoding data
Open the record for dataset details and reuse information.
Raw data and pictures for "Dramatic change in the Elasmotherium diet in the last days of life sheds light on their catastrophic mortality in the Saratov region (Russia)"
<p>Supplementary material 1 = Raw mesowear and microwear data for the <em>Elasmotherium </em>sample from Irgiz 1.</p> <p>Supplementary material 2 = Microwear pictures for the <em>Elasmotherium </em>teeth from Irgiz 1.</p>
After a catastrophe, a little bit of sex is better than nothing: genetic consequences of a major earthquake on asexual and sexual populations
<p>Catastrophic events can have profound effects on the demography of a population and consequently, on genetic diversity. The dynamics of post-catastrophic recovery as well as the role of sexual versus asexual reproduction in buffering the effects of massive perturbations remain poorly understood, in part because the opportunity to document genetic diversity before and after such events is rare. Six natural (purely sexual) and seven cultivated (mainly clonal due to farming practices) populations of the red alga Agarophyton chilense were surveyed along the Chilean coast before, in the days after and two years after the 8.8 magnitude earthquake in 2010. The genetic diversity of sexual populations appeared sensitive to this massive perturbation, notably through the loss of rare alleles immediately after the earthquake. By 2012, the levels of diversity returned to those observed before the catastrophe, probably due to migration. In contrast, enhanced rates of clonality in cultivated populations conferred a surprising ability to buffer the instantaneous loss of diversity. After the earthquake, farmers increased the already high rate of clonality to maintain the few surviving beds, but most of them collapsed rapidly. Contrasting fates between sexual and clonal populations suggest that betting on strict clonality to sustain production is risky, probably because this extreme strategy hampered adaptation to the brutal environmental perturbation induced by the catastrophe.</p>
A catastrophic tropical drought kills hydraulically vulnerable tree species
<p>Drought-related tree mortality is now a widespread phenomenon predicted to increase in magnitude with climate change. However, the patterns of which species and trees are most vulnerable to drought, and the underlying mechanisms have remained elusive, in part due to the lack of relevant data and difficulty of predicting the location of catastrophic drought years in advance. We used long‐term demographic records and extensive databases of functional traits and distribution patterns to understand the responses of 20 to 53 species to an extreme drought in a seasonally dry tropical forest in Costa Rica, which occurred during the 2015 El Niño Southern Oscillation event. Overall, species-specific mortality rates during the drought ranged from 0% to 34%, and varied little as a function of tree size. By contrast, hydraulic safety margins correlated well with probability of mortality among species, while morphological or leaf economics spectrum traits did not. This firmly suggests hydraulic traits as targets for future research.</p>
Chernobyl catastrophe in 1986 April 26 at 1:23
Chernobyl catastrophe in 1986 April 26 at 1:23 minutes and 47 seconds a terrible explosion occurred at the 4th power unit of the Chernobyl nuclear power plant Source: Objaverse 1.0 / Sketchfab
Global Catastrophic Effects on Future Climate due to Increasing Total Solar Irradiance. A General Atmospheric Circulation Analysis.
<p>10-yr CESM run with standard TSI (BGCN_T31_g37.cam.h0*)</p> <p>10-yr CESM run with TSI +10% (BGCN_T31_g37_TSI10p.cam.h0*)</p>
Reconciling the Infrared Catastrophe and Observations of SN 2011fe
<p>W7 at 300 and 1000d, and N100 at 1000d. Distance 10 Mpc assumed.</p>
Optimal Catastrophe Risk Pooling
<p>This repository contains the code to reproduce the analysis in the paper: "Increasing countries’ financial resilience through global catastrophe risk pooling" from Ciullo et al..</p>
Working code and data for a wall-to-wall mapping of carbon loss within the Chornobyl Exclusion Zone after the 2020 catastrophic wildfire
<p>Links to data in this code are from <a href="https://github.com/Janzeero-PhD/C_loss_Chornobyl_2022">GitHub repository</a>. But can be replaced by the files supplied here.</p> <p>Only code to generate models and to create figures for their validation is provided. Also code to create GMFR function (from Riemann et al. 2010 and adapted in Henderson et al. 2019) is provided.</p> <p>Code to: calculate C stock for training and validation data set per biomass compartment; prepare raster data (mostly in Google Earth Engine); make raster predictions; calculate C loss according to local combustion factors and NBR severity levels is available <em>upon request</em>.</p> <p>MetaData.xlsx contains a description of all files within this package, and variable explanation for provided data sets.</p>
Figure 3 in Mammal prevalence after the fire catastrophe in northeastern Pantanal, Brazil
Figure 3. Mammal species detected by camera traps and visual observations in periods before (2015-2017) and after fire (2021-2022) in the Pantanal of SESC Parque Baía das Pedras, Poconé, Mato Grosso, Brazil. (A) Blastocerus dichotomus; (B) Mazama sp.; (C) Pecari tajacu; (D) Tayassu pecari; (E) Cerdocyon thous; (F) Speothos venaticus; (G) Leopardus pardalis; (H) Herpailurus yagouaroundi; (I) Puma concolor; (J) Eira barbara; (K) Lontra longicaudis; (L) Nasua nasua; (M) Procyon cancrivorus; (N) Dasypus noventicus; (O) Priodontes maximus; (P) Didelphis sp.; (Q) Sylvilagus brasiliensis; (R) Tapirus terrestris; (S) Myrmecophaga tridactyla; (T) Tamandua tetradactyla; (U) Sabajus libidinosus; (V) Hydrochoerus hydrochaeris; (W) Dasyprocta azarae. Species nomenclature followed the International Union for Conservation of Nature (IUCN) red list of threatened species (IUCN, 2022).
Figure 4 in Mammal prevalence after the fire catastrophe in northeastern Pantanal, Brazil
Figure 4. Number of records of mammals obtained by camera traps before (blue) and after fire (red) in the Pantanal of SESC Parque Baía das Pedras, Poconé, Mato Grosso, Brazil. Level of significance: *** p ≤ 0.001; ** p ≤ 0.01; * p ≤ 0.05; black dot: marginal significance level.
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.